



import os
from typing import List, Optional, Union

from ...image_processing_utils import BatchFeature
from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType
from ..auto import AutoTokenizer







class 				_a				(   _lowerCAmelCase					):
			A      		=	['''image_processor''', '''tokenizer''']
			A      		=	'''BlipImageProcessor'''
			A      		=	'''AutoTokenizer'''

			def __init__(self,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)       ->   List[Any]:
						super().__init__(SCREAMING_SNAKE_CASE_,   SCREAMING_SNAKE_CASE_					)

						# add QFormer tokenizer
						UpperCAmelCase_:		str							      =     qformer_tokenizer

			def __call__(self,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = 0,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = None,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = False,   SCREAMING_SNAKE_CASE_ = True,   SCREAMING_SNAKE_CASE_ = None,   **SCREAMING_SNAKE_CASE_,   )       ->   BatchFeature:
						if images is None and text is None:
									raise ValueError("""You have to specify at least images or text."""					)

						UpperCAmelCase_:		Optional[int]							      =     BatchFeature()

						if text is not None:
									UpperCAmelCase_:		List[str]							      =     self.tokenizer(
									    text=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_,   max_length=SCREAMING_SNAKE_CASE_,   stride=SCREAMING_SNAKE_CASE_,   pad_to_multiple_of=SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   return_overflowing_tokens=SCREAMING_SNAKE_CASE_,   return_special_tokens_mask=SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   return_token_type_ids=SCREAMING_SNAKE_CASE_,   return_length=SCREAMING_SNAKE_CASE_,   verbose=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )
									encoding.update(SCREAMING_SNAKE_CASE_					)
									UpperCAmelCase_:		str							      =     self.qformer_tokenizer(
									    text=SCREAMING_SNAKE_CASE_,   add_special_tokens=SCREAMING_SNAKE_CASE_,   padding=SCREAMING_SNAKE_CASE_,   truncation=SCREAMING_SNAKE_CASE_,   max_length=SCREAMING_SNAKE_CASE_,   stride=SCREAMING_SNAKE_CASE_,   pad_to_multiple_of=SCREAMING_SNAKE_CASE_,   return_attention_mask=SCREAMING_SNAKE_CASE_,   return_overflowing_tokens=SCREAMING_SNAKE_CASE_,   return_special_tokens_mask=SCREAMING_SNAKE_CASE_,   return_offsets_mapping=SCREAMING_SNAKE_CASE_,   return_token_type_ids=SCREAMING_SNAKE_CASE_,   return_length=SCREAMING_SNAKE_CASE_,   verbose=SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_,   )
									UpperCAmelCase_:		Tuple							      =     qformer_text_encoding.pop("""input_ids"""					)
									UpperCAmelCase_:		List[Any]							      =     qformer_text_encoding.pop("""attention_mask"""					)

						if images is not None:
									UpperCAmelCase_:		str							      =     self.image_processor(SCREAMING_SNAKE_CASE_,   return_tensors=SCREAMING_SNAKE_CASE_					)
									encoding.update(SCREAMING_SNAKE_CASE_					)

						return encoding

			def      __snake_case       (self,   *SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   int:
						return self.tokenizer.batch_decode(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			def      __snake_case       (self,   *SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Union[str, Any]:
						return self.tokenizer.decode(*SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			@property
			# Copied from transformers.models.blip.processing_blip.BlipProcessor.model_input_names
			def      __snake_case       (self					)       ->   Optional[Any]:
						UpperCAmelCase_:		Optional[Any]							      =     self.tokenizer.model_input_names
						UpperCAmelCase_:		Optional[Any]							      =     self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names					)					)

			def      __snake_case       (self,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Optional[int]:
						if os.path.isfile(SCREAMING_SNAKE_CASE_					):
									raise ValueError(f'Provided path ({save_directory}) should be a directory, not a file'					)
						os.makedirs(SCREAMING_SNAKE_CASE_,   exist_ok=SCREAMING_SNAKE_CASE_					)
						UpperCAmelCase_:		int							      =     os.path.join(SCREAMING_SNAKE_CASE_,   """qformer_tokenizer"""					)
						self.qformer_tokenizer.save_pretrained(SCREAMING_SNAKE_CASE_					)
						return super().save_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)

			@classmethod
			def      __snake_case       (cls,   SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)       ->   Dict:
						UpperCAmelCase_:		Dict							      =     AutoTokenizer.from_pretrained(SCREAMING_SNAKE_CASE_,   subfolder="""qformer_tokenizer"""					)
						UpperCAmelCase_:		int							      =     cls._get_arguments_from_pretrained(SCREAMING_SNAKE_CASE_,   **SCREAMING_SNAKE_CASE_					)
						args.append(SCREAMING_SNAKE_CASE_					)
						return cls(*SCREAMING_SNAKE_CASE_					)











def  lowerCAmelCase_	(lowerCAmelCase__:		Union[str, Any]		,						lowerCAmelCase__:		Optional[int]  ):





			"""simple docstring"""


			UpperCAmelCase_:		List[str]							      =     0
			UpperCAmelCase_:		Tuple							      =     len(lowerCAmelCase__  ) - 1

			while left <= right:
						# avoid divided by 0 during interpolation
						if sorted_collection[left] == sorted_collection[right]:
									if sorted_collection[left] == item:
												return left
									else:
												return None

						UpperCAmelCase_:		Dict							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
						    sorted_collection[right] - sorted_collection[left]
						)

						# out of range check
						if point < 0 or point >= len(lowerCAmelCase__  ):
									return None

						UpperCAmelCase_:		str							      =     sorted_collection[point]
						if current_item == item:
									return point
						else:
									if point < left:
												UpperCAmelCase_:		int							      =     left
												UpperCAmelCase_:		str							      =     point
									elif point > right:
												UpperCAmelCase_:		List[str]							      =     right
												UpperCAmelCase_:		Optional[int]							      =     point
									else:
												if item < current_item:
															UpperCAmelCase_:		Optional[Any]							      =     point - 1
												else:
															UpperCAmelCase_:		Any							      =     point + 1
			return None







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict		,						lowerCAmelCase__:		List[Any]		,						lowerCAmelCase__:		Any		,						lowerCAmelCase__:		Any  ):





			"""simple docstring"""


			if sorted_collection[left] == sorted_collection[right]:
						if sorted_collection[left] == item:
									return left
						else:
									return None

			UpperCAmelCase_:		Union[str, Any]							      =     left + ((item - sorted_collection[left]) * (right - left)) // (
			    sorted_collection[right] - sorted_collection[left]
			)

			# out of range check
			if point < 0 or point >= len(lowerCAmelCase__  ):
						return None

			if sorted_collection[point] == item:
						return point
			elif point < left:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			elif point > right:
						return interpolation_search_by_recursion(lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__  )
			else:
						if sorted_collection[point] > item:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						lowerCAmelCase__		,						point - 1  )
						else:
									return interpolation_search_by_recursion(
									    lowerCAmelCase__		,						lowerCAmelCase__		,						point + 1		,						lowerCAmelCase__  )







def  lowerCAmelCase_	(lowerCAmelCase__:		Dict  ):





			"""simple docstring"""


			if collection != sorted(lowerCAmelCase__  ):
						raise ValueError("""Collection must be ascending sorted"""  )
			return True


if __name__ == "__main__":
	import sys
	a      :  Optional[Any]			     =			0
	if debug == 1:
		a      :  int			     =			[10, 30, 40, 45, 50, 66, 77, 93]
		try:
			__assert_sorted(collection)
		except ValueError:
			sys.exit('Sequence must be ascending sorted to apply interpolation search')
		a      :  int			     =			67

	a      :  Any			     =			interpolation_search(collection, target)
	if result is not None:
		print(F'''{target} found at positions: {result}''')
	else:
		print('Not found')






